Call preparation engine for customer relationship management

ABSTRACT

Call preparation engine for customer relationship management (“CRM”) is presented. Example embodiments of the present invention include invoking an intelligence assistant to retrieve lead details, customer information, and insights for use during a call between a tele-agent and a customer; administering tele-agent call preparation notes for use during the call between the tele-agent and the customer; and displaying, through a call preparation cockpit, the lead details, customer information, insights and tele-agent call preparation notes.

ADDITIONAL APPLICATIONS

The following U.S. patent applications, having subject matter somewhatrelevant to this application, are hereby for all purposes incorporatedby reference into this application as though fully set forth herein:

-   U.S. patent application Ser. No. 15/700,210; filed Sep. 11, 2017,    entitled Dynamic Scripts for Tele-Agents;-   U.S. patent application Ser. No. 15/844,512; filed Dec. 15, 2017,    entitled Dynamic Lead Generation;-   U.S. patent application Ser. No. 16/157,075; filed Oct. 10, 2018,    entitled Semantic Jargon;-   U.S. patent application Ser. No. 16/154,718 filed Oct. 9, 2018,    entitled Semantic Call Notes;-   U.S. patent application Ser. No. 16/177,423 filed Oct. 31, 2018,    entitled Semantic Inferencing in Customer Relationship Management;-   U.S. patent application Ser. No. 16/183,725, filed Nov. 7, 2018,    entitled Semantic CRM Mobile Communications Sessions;-   U.S. patent application Ser. No. 16/183,736, filed Nov. 8, 2018,    entitled Semantic Artificial Intelligence Agent;-   U.S. patent application Ser. No. 16/198,742 filed Nov. 21, 2018,    entitled Semantic CRM Transcripts from Mobile Communications    Sessions-   U.S. patent application Ser. No. 16/947,802 filed Aug. 17, 2020,    entitled Asynchronous Multi-Dimensional Platform For Customer And    Tele-Agent Communications;-   U.S. patent application Ser. No. 16/776,4882 filed Jan. 29, 2020,    entitled Agnostic Augmentation of a Customer Relationship Management    Application;-   U.S. patent application Ser. No. 16/857,295 filed Apr. 24, 2020,    entitled Agnostic Customer Relationship Management with Agent Hub    and Browser Overlay;-   U.S. patent application Ser. No. 16/857,321 filed Apr. 24, 2020,    entitled Agnostic CRM Augmentation with a Display Screen-   U.S. patent application Ser. No. 16/857,341 filed Apr. 24, 2020,    entitled Agnostic Customer Relationship Management with Browser    Overlay and Campaign Management Portal-   U.S. patent application Ser. No. 17/248,314 filed Apr. 24, 2020,    entitled Product Presentation for Customer Relationship Management

BACKGROUND

Customer Relationship Management (CRM′) is an approach to managing acompany's interaction with current and potential customers. CRM oftenimplements data analysis of customers' history with a company to improvebusiness relationships with customers, specifically focusing on customerretention and sales growth. CRM systems often compile data from a rangeof communication channels, including telephone, email, live chat, textmessaging, marketing materials, websites, and social media. Through theCRM approach and the systems used to facilitate it, businesses learnmore about their target audiences and how to best address their needs.

Enterprise CRM systems can be huge. Such systems can include datawarehouse technology, used to aggregate transaction information, tomerge the information with information regarding CRM products andservices, and to provide key performance indicators. CRM systems aidmanaging volatile growth and demand and implement forecasting modelsthat integrate sales history with sales projections. CRM systems trackand measure marketing campaigns over multiple networks, trackingcustomer analysis by customer clicks and sales.

In view of their sheer size, CRM systems today tend to lack theinfrastructure to make full use of the information they can access. Asingle tele-agent, working in a contact center supporting a hundredagents, is tasked to operate across several, or even many, client CRMsystems, each having different interfaces. Furthermore, conventional CRMsystems often do not make use of information available to the enterpriseand in turn do not present that information to a tele-agent to improvethe performance and efficiency of the agent.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 sets forth a network diagram illustrating an example system forcall preparation for customer relationship management (CRM′) accordingto embodiments of the present invention.

FIG. 2 sets forth a line drawing of an example system for callpreparation according to embodiments of the present invention.

FIG. 3 sets forth a system diagram of a system for call preparationaccording to embodiments of the present invention.

FIG. 4 sets forth a line drawing of a graph.

FIG. 5 sets forth a functional block diagram of an example apparatus forcall preparation in a thin-client architecture according to embodimentsof the present invention.

FIG. 6 sets forth a block diagram of automated computing machinerycomprising an example of a computer useful as a voice server for aspeech-enabled device useful in call preparation according toembodiments of the present invention.

FIG. 7 sets forth a block diagram of automated computing machinerycomprising an example of a computer useful as a triple server for callpreparation according to embodiments of the present invention.

FIG. 8 sets forth a functional block diagram of an example apparatus forcall preparation for CRM in computer memory in a thick-clientarchitecture according to embodiments of the present invention.

FIG. 9 sets forth a flow chart illustrating an example method for callpreparation for customer relationship management (‘CRM’) according toembodiments of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Example methods, systems, apparatuses, and products for call preparationfor customer relationship management are described with reference to theaccompanying drawings, beginning with FIG. 1. FIG. 1 sets forth anetwork diagram illustrating an example system for call preparation forcustomer relationship management (CRM′) according to embodiments of thepresent invention.

Call preparation for customer relationship management (‘CRM’) in theexample of FIG. 1 is implemented with at least one speech-enabled device(152), a triple server (157), and a voice server (151). A speech-enableddevice is automated computing machinery configured to accept andrecognize speech from a user and express to a user voice prompts andspeech responses. Speech-enabled devices in the example of FIG. 1include a desktop computer (107), a smartwatch (112), a mobile phone(110), a laptop computer (126), and an enterprise server (820) operatinga call preparation engine (800) according to embodiments of the presentinvention. Each speech-enabled device in this example is coupled fordata communications through network (100) to the triple server (157) andthe voice server (151). The desktop computer (107) and the enterpriseserver (820) operating the call preparation engine (802) are connectedthrough wireline connections (120,121), while the smartwatch (112),mobile phone (110), and laptop (126) are connected respectively throughwireless connections (114, 116, 118). Each of the connections, wired orwireline, in the example of FIG. 1 are for explanation and not forlimitation. The devices, applications, and other components of FIG. 1may be coupled for data communications in many ways as will occur tothose of skill in the art.

The overall example system illustrated in FIG. 1 operates generally forcall preparation according to embodiments of the present invention byinvoking an intelligence assistant (300) to retrieve lead details,customer information, insights, and other information for use during acall between a tele-agent and a customer; administering tele-agent callpreparation notes for use during the call between the tele-agent and thecustomer; and displaying the lead details, customer information,insights, and call preparation notes.

As mentioned above, call preparation according to various embodiments ofthe present invention is speech-enabled. Often the speech forrecognition is dictated by a tele-agent in preparation of a call betweenthe tele-agent and a customer or the speech of that conversation itself.A word (509) of digitized speech in this example is speech forrecognition from a tele-agent (128) or a conversation between thetele-agent (128) and a customer. The speech for recognition can be anentire conversation, where, for example, all persons speaking are in thesame room, and the entire conversation is picked up by a microphone on aspeech-enabled device. The scope of speech for recognition can bereduced by providing to a speech-enabled device conversation from onlyto one person or a person on one side of a conversation, as only througha microphone on a headset. The scope of speech for recognition can bereduced even further by providing for recognition only speech thatresponds to a prompt from, for example, a VoiceXML dialogue executing ona speech-enabled device. As the scope of speech for recognition isreduced, data processing burdens are reduced across the system as awhole, although it remains an option, in some embodiments at least, torecognize entire conversations and stream across flow of all words inthe conversation.

Speech from a tele-agent or from a conversation is recognized intodigitized speech by operation of a natural language processing speechrecognition (“NLP-SR”) engine (153), shown here disposed upon a voiceserver (151), but also amenable to installation on speech-enableddevices. The NLP-SR engine also carries out the parsing of a word (509)of the speech so digitized (508) into a triple (752) of a descriptionlogic.

A triple is a three-part statement expressed in a form of logic.Depending on context, different terminologies are used to refer toeffectively the same three parts of a statement in a logic. In firstorder logic, the parts are called constant, unary predicate, and binarypredicate. In the Web Ontology Language (“OWL”) the parts areindividual, class, and property. In some description logics the partsare called individual, concept, and role.

In this example description, the elements of a triple are referred to assubject, predicate, and object—and expressed like this:<subject><predicate><object>. There are many modes of expression fortriples. Elements of triples can be represented as Uniform ResourceLocaters (“URLs”), Uniform Resource Identifiers (“URIs”), orInternational Resource Identifiers (“IRIs”). Triples can be expressed inN-Quads, Turtle syntax, TriG, Javascript Object Notation or “JSON,” thelist goes on and on. The expression used here, subject-predicate-objectin angle brackets, is one form of abstract syntax, optimized for humanreadability rather than machine processing, although its substantivecontent is correct for expression of triples. Using this abstractsyntax, here are examples of triples:

<Bob> <is a> <person> <Bob> <is a friend of> <Alice> <Bob> <is born on><the 4^(th) of July 1990> <Bob> <is interested in> <the Mona Lisa> <theMona Lisa> <was created by> <Leonardo da Vinci > <the video ‘La Jocondeà Washington’ <is about> <the Mona Lisa>

The same item can be referenced in multiple triples. In this example,Bob is the subject of four triples, and the Mona Lisa is the subject ofone triple and the object of two. This ability to have the same item bethe subject of one triple and the object of another makes it possible toeffect connections among triples, and connected triples form graphs.

The example of FIG. 1 includes a semantic graph database (818) whichincludes an enterprise knowledge graph (816). A semantic graph is aconfiguration of memory that uses graph structures, nodes and edges, torepresent and store data. A key concept of this kind of configuration isthe graph (or edge or relationship), which directly relates data itemsin a data store. Such a graph database contrasts with more conventionalstorage such as a logical table, where links among data are mereindirect metadata, and queries search for data within the store usingjoins to collect related data. Semantic graphs, by design, make explicitrelations among data that can be difficult to model in relationalsystems or logical tables.

In the example of FIG. 1, the semantic graph database (816) includes asemantic triple store (814). The semantic triple store (804) of FIG. 1includes triple stores for access by the intelligence assistant (300),the CRM (806) and the call preparation engine (800). The triple store(814) of FIG. 1 contains structured definitions of words not special toany particular knowledge domain, where each structured definition of thegeneral language store is implemented with a triple of descriptionlogic. The triple store (814) also includes structured definitions ofwords for recognition in particular knowledge domains such as products,jargon of an industry, particular industries, geographic areas, and soon, where each structured definition of the product triple store isimplemented with a triple of description logic.

The semantic triple store (814) in the example of FIG. 1 includestriples defining various forms of information useful in call preparationaccording to embodiments of the present invention. Such triples may bequeried by an intelligence assistant invoked by a call preparationengine to retrieve insights, call preparation notes parsed into semantictriples, customer connections, relevant use cases, chats, installedtechnology of a customer, a talk track, product recommendations, agentscoring, call goals, dynamic scripts, and so on as will occur to thoseof skill in the art. The information stored in knowledge graph (816) ofFIG. 1 is presented for explanation and not for limitation. Theenterprise knowledge graph may be used to store other information usefulin call preparation according to embodiments of the present invention aswill occur to those of skill in the art.

As mentioned above, the example of FIG. 1 includes a CRM (806). Such aCRM is a CRM system configured for the use of tele-agents and otherusers of the enterprise. Often data stored on and accessed by the CRM isdata owned by the enterprise itself and collected over time for the useof various users of the organization as will occur to those of skill inthe art. In other embodiments of the present invention, the CRM may beowned by a client of the call center and the data residing in that CRMis owned by the client.

The example of FIG. 1 includes a call preparation engine (800). The callpreparation engine of FIG. 1 is a module of automated computingmachinery stored on one or more non-transitory computer-readablemediums. The call preparation engine (800) of FIG. 1 includes a callpreparation application (802) and a call preparation cockpit application(804). The call preparation application (802) comprises a module ofautomated computing machinery stored on one or more non-transitorycomputer-readable mediums (159) and configured to invoke theintelligence assistant (300) to retrieve lead details, customerinformation, and insights for use during a call between a tele-agent anda customer and to administer tele-agent call preparation notes for useduring the call between the tele-agent and the customer.

The call preparation cockpit application (804) also comprises a moduleof automated computing machinery stored on one or a non-transitorycomputer-readable mediums (159) and configured to receive, from the callpreparation application (802), lead details, insights, and callpreparation notes for use during the between a tele-agent and a customerand display, through a call preparation cockpit (110), the lead details(852). the insights (864), and the call preparation notes.

The example of FIG. 1 also includes an intelligence assistant (300). Theintelligence assistant of FIG. 1 is a speech-enabled platform capable ofinsight generation and management of the semantic graph database asdiscussed in more detail below with reference to FIG. 3. Theintelligence assistant (300), the CRM (806) and the call preparationapplication (802) are connected for data communications to an enterpriseserver (820), a triple server (157), a voice server (151), a lead engine(134), a social media server (130), and an industry server (130).

In the example of FIG. 1, the intelligence assistant retrieves leadsfrom a third-party operating the lead engine (134). Such leads mayprovide information regarding customers often including contactinformation, key employees, site locations and other informationregarding the customer as will occur to those of skill in the art. Theleads so retrieved are parsed into semantic triples and stored thesemantic triple store (814) of the enterprise knowledge graph (816).

The intelligence assistant (300), CRM (806), and the call preparationapplication (802) of FIG. 1 are connected for data communications withone or more social media servers (130). Such social media servers areoften implemented by third parties and often provide information andinsight about their users. Examples of social media companies includelinkedIn, Facebook, Instagram, and others as will occur to those ofskill in the art. In the example of FIG. 1, the intelligence assistant(300) may receive information directly from the social media servers orindirectly from the CRM or call preparation engine and parse theinformation into semantic triples for storage in the semantic triplestore of the enterprise knowledge graph.

The CRM (806) and call preparation application of FIG. 1 is connectedfor data communications with one or more industry servers. Such serversare often operated by third parties and provide current and historicinformation regarding a particular industry, companies in variousindustries and so on as will occur those of skill in the art. In theexample of FIG. 1, the intelligence assistant (300) may receiveinformation directly from the industry servers or indirectly from theCRM or call preparation engine and parse the information into semantictriples for storage in the semantic triple store of the enterpriseknowledge graph.

The use of a lead server (824), social media server (826) and theindustry server (828) in the example of FIG. 1 is for ease ofexplanation and not for limitation. In fact, call preparation accordingto embodiments of the present invention may make use of many third-partysystems or internal systems as will occur to those of skill in the art.

The example of FIG. 1 includes a call preparation cockpit (110) for thetele-agent's use in preparation for a call with a customer and during aconversation between the tele-agent (128) and the customer (129). Thecall preparation cockpit (110) is a speech-enabled user interface uponand through which the call preparation engine (800) provides real-timecall preparation information for display to the tele-agent (128) throughthe dashboard (110) for the tele-agent's use in preparing for a callwith a customer and during a conversation between the tele-agent (128)and the customer.

Call preparation information may be in many different forms and comefrom both internal proprietary data stores of the enterprise, as well asinformation retrieved by the CRM and the call preparation applicationfrom third-party providers.

In the example of FIG. 1, many components useful in call preparationaccording to embodiments of the present invention are maintained incomputer memory (159). In the example of FIG. 1, computer memory (159)includes cache, random access memory (“RAM”), disk storage, and so on,most forms of computer memory. Computer memory (159) so configuredtypically resides upon speech-enabled devices, or as shown here, uponone or more triple servers (157), voice servers, or enterprise servers(820)

For further explanation, FIG. 2 sets forth a line drawing of an examplesystem for call preparation according to embodiments of the presentinvention. The example of FIG. 2 includes a semantic graph database(818) which includes an enterprise knowledge graph (816). The semanticgraph database (818) and enterprise knowledge graph (816) maintains adata store of proprietary and non-proprietary information regarding,among other things, customer information, products that may be discussedbetween a tele-agent (128) and customers, recommended products for thecustomer, tele-agent nots for user during the call so on as will occurto those of skill in the art. Such information may be provided to thecall preparation application who in turns proves information to callpreparation cockpit application (804) and the call preparation cockpitapplication (804) displays onto the call preparation cockpit (110)making the information available of use by the tele-agent (128) inreal-time both before and during a conversation between the tele-agentand the customer.

The example of FIG. 2 also includes a CRM (806) having a data store ofproprietary and non-proprietary information regarding, among otherthings, customer information, products that may be discussed between atele-agent (128) and customers, recommended products for the customer,tele-agent nots for user during the call so on as will occur to those ofskill in the art. Such information may be provided to the callpreparation application who in turns proves information to callpreparation cockpit application (804) and the call preparation cockpitapplication (804) displays onto the call preparation cockpit (110)making the information available of use by the tele-agent (128) inreal-time both before and during a conversation between the tele-agentand the customer.

The example of FIG. 2 includes an intelligence assistant (300), atargeted collection of artificial intelligence-based technologiesincluding natural and semantic language processing that processesunstructured communications into structured information that generates,in dependence upon the structured information and CRM data, insightsavailable to the call preparation engine, ultimately driving improvedquality and efficiency of the tele-agent. The intelligence assistant(300) administers an enterprise knowledge graph (816) of a semanticgraph database that houses structured data in the form of triplesoptimized for insight generation.

The example of FIG. 2 includes a call preparation engine (800) thatincludes a call preparation application (802) and a call preparationcockpit application (804). The call preparation engine (900) of FIG. 2is a module of automated computing machinery stored on one or morenon-transitory computer-readable mediums. The call preparationapplication (802) operates generally by invoking an intelligenceassistant (300) to retrieve lead details, customer information,insights, and other information for use during a call between atele-agent and a customer; administering tele-agent call preparationnotes for use during the call between the tele-agent and the customer.

The call preparation cockpit application (804) of FIG. 2 comprises amodule of automated computing machinery stored on one or anon-transitory computer-readable mediums (159) and operates generally byreceiving and displaying lead details, customer information, insights,call preparation notes and other information as discussed herein.

In the example of FIG. 2, the tele-agent is provided real-time customerand product information displayed through the call preparation cockpit(110) for real-time use by the tele-agent in both preparation for a callwith a customer and for use during the conversation with the customer.In the example of FIG. 2, the speech-enabled dashboard includes a widgetfor the display of the tele-agent's call preparation notes (850). Suchcall notes may be prepared or supplemented by the tele-agent before,during, or after a conversation with the customer.

Widgets in this disclosure are implemented as software applications orcomponents that perform one or more particular tasks and whose executionis administered by the call preparation engine, often through the callpreparation application or the call preparation cockpit. The widgets ofFIG. 2 are described as being administered by the call preparationcockpit (110), but this is for ease of explanation and not forlimitation. Each of the widgets described in this disclosure have anaccompanying GUI element as illustrated in FIG. 2. The example widgetsand their associated GUI elements are for explanation and not forlimitation. In fact, call preparation engines according to embodimentsof the present invention may administer widgets that have no GUIelements or have more than one GUI element as will occur to those ofskill in the art.

In the example of FIG. 2, the speech-enabled call preparation cockpit(110) includes a widget for the display of the lead details (852)including information describing the customer and customer's businessoften including name and contact information of the customer, theindustry supported by the customer, the locations the customer conductsbusiness, and other useful information that may be included in a listingof facts (860) about the customer. The cockpit (110) of FIG. 2 alsodisplays a connections image (862) that provides to the tele-agent (128)any known connections between the customer and other customers or peopleor organizations traced within the CRM (806).

In the example of FIG. 2, the speech-enabled call preparation cockpit(110) includes a widget for the display of a calendar (882) and widgetfor CRM insights (864) displaying insights regarding the customer knownor derived by the CRM (806). In the example of FIG. 2, thespeech-enabled dashboard (110) includes a widget for the display of thetechnology (858) currently installed in the customer's sites andlocations useful in discussing products that are compatible with thatinstalled technology with the customer.

In the example of FIG. 2, the speech-enabled call preparation cockpit(110) includes a widget for the display of use cases (880) describingthe product that may be useful to the tele-agent during a conversationwith the customer. Often such use cases are provided by clients of thecall center to better inform customers about the products the clientsells. Similarly, such clients may provide other collateral that may beuseful in communicating with the customer and such collateral may comein many forms as will occur to those of skill in the art.

In the example of FIG. 2, the speech-enabled cockpit (110) includes awidget for the display of product recommendations (852) for the customerand a display of competing products (868) that may be sold bycompetitors of the tele-agent that are compatible with the currentlyinstalled technology already in use by the customer. These productrecommendations may be useful to a tele-agent (128) in preparation foror during a conversation with the customer. Such product recommendationsmay be provided by the semantic graph database (816), the CRM (806), thecall preparation engine (800) or from other sources as will occur tothose of skill in the art.

In the example of FIG. 2, the speech-enabled call preparation cockpit(110) includes a widget for the display of industry insights. Industryinsights may include current trends in the customer's industry, newlyavailable products for use in an industry, current news regarding theindustry and so on as will occur to those of skill in the art. Suchinsights may be stored and managed by the semantic graph database (816),the CRM (806), the call preparation engine (800), or provided from thirdparties such as third parties operating industry servers, social mediaservers, lead servers and many others as will occur to those of skill inthe art.

In the example of FIG. 2, the speech-enabled call preparation cockpit(110) includes a widget for the display of a talk track. The talk trackincludes a campaign specific introduction for a phone call with thecustomer and optionally additional talking points for the tele-agent. Insome cases, a tele-agent may want to customize a talk track to includepreferred vernacular, style, and other attributes. The talk track iseditable in the example of FIG. 2 by the tele-agent through speech or byuse of a keyboard.

In the example of FIG. 2, the speech-enabled call preparation cockpit(110) includes a widget for the display of subject matter expertsuggestions (874). Subject matter expert suggestions (874) may beprovided in real time or maintained by the semantic graph database(816), the CRM (806), the call preparation engine (800) of received fromother sources as will occur to those of skill in the art. Subject matterexpert suggestions may be provided by internal subject matter experts orby third-party subject matter experts or in other ways as will occur tothose of skill in the art.

In the example of FIG. 2, the speech-enabled call preparation cockpit(110) includes a widget for agent scoring (876). An agent score mayrepresent the tele-agents place along a defined sales cycle of acampaign, the tele-agents rank among other agents in the call center orenterprise, as well as other ways of scoring the tele-agent as willoccur to those of skill in the art. Such an agent score may be developedand maintained by the semantic graph database (816), the CRM (806), thecall preparation engine (800), or other components as will occur tothose of skill in the art.

In the example of FIG. 2, the speech-enabled call preparation cockpit(110) includes a widget for the display of call goals (878) for thetele-agent. Such call goals are typically provided by the CRM (806) orthe call preparation application (802) and are often related to thetele-agents place in a sales cycle defined for the particular campaign.

In the example of FIG. 2, the speech-enabled call preparation cockpit(110) includes a widget for the display of a dynamic script (854) oftencreated in real-time as a guidance and aid for the tele-agent in theconversation with the customer. Such a script may be created by the CRM(806), the call preparation engine (800) or other components based uponthe current sales campaign, information relating to the customer,historic sales trends, success stories of tele-agents and may otherfactors as will occur to those of skill in the art.

In the example of FIG. 2, the speech-enabled dashboard (110) includes awidget for the display of a barometer (860) the barometer is graphicrepresentation or text display providing the tele-agent with anindication of the current performance of the tele-agent servicing aparticular sales campaign. Such a barometer may be created by thesemantic graph database (816), CRM (806), call preparation engine (800)or other components based on many factors such as the goals of the salescampaign, the performance of the tele-agent or other the tele-agentsservicing the sales campaign and many other factors as will occur tothose of skill in the art.

The components and widgets presented in the example of FIG. 2 are forexplanation and not for limitation. Components and widgets, as well asthe functions they perform and information they provide may varydramatically among various embodiments of the present invention as willoccur to those of skill in the art. All such components and widgetswhatever their form may be used for call preparation for CRM accordingto various embodiments of the present invention.

For further explanation, FIG. 3 sets forth a system diagram illustratinga system for call preparation according to embodiments of the presentinvention. The system of FIG. 3 includes a call preparation engine(800), a computer (102), a CRM (806), a speech engine (153), anintelligence assistant (300), and a semantic graph database (818)networked for data communications. The call preparation engine (800) isa module of automated computing machinery configured to invoke anintelligence assistant (300) to retrieve lead details, customerinformation, and insights for use during a call between a tele-agent anda customer, administer tele-agent call preparation notes for use duringthe call between the tele-agent and the customer, and display, through acall preparation cockpit, the lead details, customer information,insights and tele-agent call preparation notes.

The intelligence assistant (300) of FIG. 3 is a targeted collection ofartificial intelligence-based technologies including natural andsemantic language processing that processes unstructured communicationsinto structured information and generates in dependence upon thestructured information and CRM data insights available to the callpreparation engine, ultimately driving improved quality and efficiencyof the tele-agent.

The CRM (806) of FIG. 3 is automated computing machinery that providescontact management, sales management, agent productivity administration,and other services targeted to improved customer relations andultimately customer satisfaction and enterprise profitability. Theexample CRM of FIG. 3 manages manage customer relationships across theentire customer lifecycle, individual sales cycles, campaigns, drivingmarketing, sales, and customer service and so on.

The semantic graph database (818) of FIG. 3 is a type of graph databasethat is capable of integrating heterogeneous data from many sources andmaking links between datasets. It focuses on the relationships betweenentities and is able to infer new knowledge out of existing information.There semantic graph database of FIG. 3 is used to infer or understandthe meaning of information. The semantic technology of FIG. 3 can linknew information automatically, without manual user intervention or thedatabase being explicitly pre-structured. This automatic linking ispowerful when fusing data from inside and outside company databases,such as corporate email, documents, spreadsheets, customer support logs,relational databases, government/public/industry repositories, newsfeeds, customer data, social networks and much more. In traditionalrelational databases this linking involves complex coding, datawarehouses and heavy pre-processing with exact a priori knowledge of thetypes of queries to be asked.

The semantic graph database (818) of FIG. 3 includes a databasemanagement system ‘DBMS’ (316) and data storage (320). The DBMS of FIG.3 includes an enterprise knowledge graph (816) and a query engine (314).The enterprise knowledge graph of FIG. 3 is a structured representationof data stored in data storage (320). The query engine of FIG. 3receives structured queries and retrieves stored information inresponse.

The system of FIG. 3 includes a speech engine (153). The example speechengine includes an NLP engine and ASR engine for speech recognition andtext-to-speech (‘TTS’) for generating speech. The example speech engine(153) includes a grammar (104), a lexicon (106), and a language-specificacoustic model (108) as discussed in more detail below.

The intelligence assistant (300) of FIG. 3 includes a triple parser andserializer (306). The triple parser of FIG. 3 takes as input a file insome format such as the standard RDF/XML format, which is compatiblewith the more widespread XML standard. The triple parser takes such afile as input and converts it into an internal representation of thetriples that are expressed in that file. At this point, the triples arestored in the triple store are available for all the operations of thatstore. Triples parsed and stored in the triple store can be serializedback out using the triple serializer (306).

The triple parser of Figure creates triples in dependence upon ataxonomy (304) and an ontology (308). The taxonomy (304) includes wordsor sets of words with defined semantics that will be stored as triples.To parse speech into semantic triples the triple parser receives textconverted from speech by the speech engine and identifies portions ofthat text that correspond with the taxonomy and forms triples using thedefined elements of the taxonomy.

The triple parser of FIG. 3 also creates triples in dependence upon anontology (308). An ontology is a formal specification that providessharable and reusable knowledge representation. An ontologyspecification includes descriptions of concepts and properties in adomain, relationships between concepts, constraints on how therelationships can be used and other concepts and properties.

The intelligence assistant (300) of FIG. 3 includes an insight generator(310). The insight generator (310) of FIG. 3 queries the query engine(314) of the semantic graph database (818) and identifies insights independence upon the results of the queries. Such insights may beselected from predefined insights meeting certain criteria of the searchresults or may be formed from the query results themselves. Suchinsights may be useful to a tele-agent during a conversation with acustomer. Examples of insights useful in call preparation includeinformation about an industry, a customer job title, insights relatingto budget, authority, need, and time (‘BANT’), cost and pricinginformation, competitive products, positive or negative sentiment incall, chat, email or other communication, identification of keyindividuals, targets reached, contacts reached, industry terms, nextbest action for a tele-agent, product recommendations, custom metrics,and many others as will occur to those of skill in the art. Insightgenerators according to embodiments of the present invention generatequeries using a query language. Query languages may be implemented as anRDF query language such as SPARQL.

The intelligence assistant (300) of FIG. 3 includes a third-party dataretrieval module (312). The third-party data retrieval module (312) ofFIG. 3 is a module of automated computing machinery configured toretrieve information from third-party resources such as industryservers, social media servers, clients, customers, and other resourcesas will occur to those of skill in the art.

For further explanation of relations among triples and graphs, FIG. 4sets forth a line drawing of a graph (600). The example graph of FIG. 4implements in graph form the example triples set forth above regardingBob and the Mona Lisa. In the example of FIG. 4, the graph edges (604,608, 612, 616, 620, 624) represent respectively relations among thenode, that is, represent the predicates <is a>, <is a friend of>, <isborn on>, <is interested in>, <was created by>, and <is about>. Thenodes themselves represent the subjects and objects of the triples,<Bob>, <person>, <Alice>, <the 4th of July 1990>, <the Mona Lisa>,<Leonardo da Vinci>, and <the video ‘La Joconde à Washington’>.

In systems of knowledge representation, knowledge represented in graphsof triples, including, for example, knowledge representationsimplemented in Prolog databases, Lisp data structures, or inRDF-oriented ontologies in RDFS, OWL, and other ontology languages.Search and inference are effected against such graphs by search enginesconfigured to execute semantic queries in, for example, Prolog orSPARQL. Prolog is a general-purpose logic programming language. SPARQLis a recursive acronym for “SPARQL Protocol and RDF Query Language.”Prolog supports queries against connected triples expressed asstatements and rules in a Prolog database. SPARQL supports queriesagainst ontologies expressed in RDFS or OWL or other RDF-orientedontologies. Regarding Prolog, SPARQL, RDF, and so on, these are examplesof technologies explanatory of example embodiments of the presentinvention. Thus, such are not limitations of the present invention.Knowledge representations useful according to embodiments of the presentinvention can take many forms as may occur to those of skill in the art,now or in the future, and all such are now and will continue to be wellwithin the scope of the present invention.

A description logic is a member of a family of formal knowledgerepresentation languages. Some description logics are more expressivethan propositional logic but less expressive than first-order logic. Incontrast to first-order logics, reasoning problems for descriptionlogics are usually decidable. Efficient decision procedures thereforecan be implemented for problem of search and inference in descriptionlogics. There are general, spatial, temporal, spatiotemporal, and fuzzydescriptions logics, and each description logic features a differentbalance between expressivity and reasoning complexity by supportingdifferent sets of mathematical constructors.

Search queries are disposed along a scale of semantics. A traditionalweb search, for example, is disposed upon a zero point of that scale, nosemantics, no structure. A traditional web search against the keyword“derivative” returns HTML, documents discussing the literary concept ofderivative works as well as calculus procedures. A traditional websearch against the keyword “differential” returns HTML pages describingautomobile parts and calculus functions.

Other queries are disposed along mid-points of the scale, somesemantics, some structure, not entirely complete. This is actually acurrent trend in web search. Such systems may be termed executablerather than decidable. From some points of view, decidability is not aprimary concern. In many Web applications, for example, data sets arehuge, and they simply do not require a 100 percent correct model toanalyze data that may have been spidered, scraped, and converted intostructure by some heuristic program that itself is imperfect. People useGoogle because it can find good answers a lot of the time, even if itcannot find perfect answers all the time. In such rough-and-tumblesearch environments, provable correctness is not a key goal.

Other classes of queries are disposed where correctness of results iskey, and decidability enters. A user who is a tele-agent in a datacenter speaking by phone with an automotive customer discussing a frontdifferential is concerned not to be required to sort through calculusresults to find correct terminology. Such a user needs correctdefinitions of automotive terms, and the user needs query results inconversational real time, that is, for example, within seconds.

In formal logic, a system is decidable if there exists a method suchthat, for every assertion that can be expressed in terms of the system,the method is capable of deciding whether or not the assertion is validwithin the system. In practical terms, a query against a decidabledescription logic will not loop indefinitely, crash, fail to return ananswer, or return a wrong answer. A decidable description logic supportsdata models or ontologies that are clear, unambiguous, andmachine-processable. Undecidable systems do not. A decidable descriptionlogic supports algorithms by which a computer system can determineequivalence of classes defined in the logic. Undecidable systems do not.Decidable description logics can be implemented in C, C++, SQL, Lisp,RDF/RDFS/OWL, and so on. In the RDF space, subdivisions of OWL vary indecidability. Full OWL does not support decidability. OWL DL does.

For further explanation, FIG. 5 sets forth a functional block diagram ofexample apparatus for call preparation in a thin-client architectureaccording to embodiments of the present invention. A thin-clientarchitecture is a client-server architecture in which at least some of,perhaps most of, perhaps all of, speech processing and triple processingis off-loaded from the client to servers. Thinness of a thin clientvaries. The speech-enabled device in the example of FIG. 5 is a thinclient in which most speech processing is off-loaded to a voice server(151). The speech-enabled device (152) accepts voice input (315, 176),but then transfers the voice input through a VOIP connection (216) tothe voice server (151) where all speech processing is performed. Thespeech-enabled device in this example does implement some capacity fortriple processing (323, 325) and query execution (298), but none of thatwould be absolutely necessary in a thin client. Devices with reducedstorage capacity, a smartwatch or a mobile phone for example, can beimplemented with no semantic query engine (298) and no triple store(323, 325), merely passing queries through to a triple server (157) thatitself carries out all triple storage and all query processing.

In the particular example of FIG. 5, the speech-enabled device occupiesthe middle ground of thin-client architecture. It supports little speechprocessing, but it does support some triple processing. Thespeech-enabled device in this example performs triple processing andquery execution only against triple stores in RAM (168), leavinglarge-scale storage to the triple server (157). The semantic queryengine loads the triple stores as needed to respond to queries. Thus,there are query misses. When the semantic query engine cannot satisfy aquery with the triple stores in RAM, it does not conclude failure.Instead, it passes the query to the triple server, and, if the tripleserver can satisfy the query by use of triples on the server, it passesback to the speech-enabled device both the query results and the triplesthat satisfied the query, which triples are then stored in RAM on thespeech-enabled device for use against similar queries in the future.Over time, such an architecture builds on the speech-enabled devicequery stores containing frequently useful triples and reduced the needfor costly query trips across the network to the triple server—while atthe same time functioning with a relatively thin layer of computingresources on the client side. This is a compromise between the extremelythin client with no triple storage at all and the thick client describedbelow with regard to FIG. 8.

The example apparatus of FIG. 5 includes a speech-enabled device (152)and a voice server (151) connected for data communication by a VOIPconnection (216) through a data communications network (100). Aspeech-enabled application (195) runs on the speech-enabled device(152). The speech-enabled application (195) may be implemented as a setor sequence of X+V or SALT documents that execute on a speech-enabledbrowser, a Java Voice application that executes on a Java VirtualMachine, or a speech-enabled application implemented in othertechnologies as may occur to those of skill in the art. The examplespeech-enabled device of FIG. 4 also includes a sound card (174), whichis an example of an I/O adapter specially designed for accepting analogaudio signals from a microphone (176) and converting the audio analogsignals to digital form for further processing by a codec (183).

VOIP stands for ‘Voice Over Internet Protocol,’ a generic term forrouting speech over an IP-based data communications network. The speechdata flows over a general-purpose packet-switched data communicationsnetwork, instead of traditional dedicated, circuit-switched voicetransmission lines. Protocols used to carry voice signals over the IPdata communications network are commonly referred to as ‘Voice over IP’or ‘VOIP’ protocols. VOIP traffic may be deployed on any IP datacommunications network, including data communications networks lacking aconnection to the rest of the Internet, for instance on a privatebuilding-wide local area data communications network or ‘LAN.’

Many protocols may be used to effect VOIP, including, for example, typesof VOIP effected with the IETF's Session Initiation Protocol (‘SIP’) andthe ITU's protocol known as ‘H.323.’ SIP clients use TCP and UDP port5060 to connect to SIP servers. SIP itself is used to set up and teardown calls for speech transmission. VOIP with SIP then uses RTP fortransmitting the actual encoded speech. Similarly, H.323 is an umbrellarecommendation from the standards branch of the InternationalTelecommunications Union that defines protocols to provide audio-visualcommunication sessions on any packet data communications network.

A speech-enabled application is typically a user-level, speech-enabled,client-side computer program that presents a voice interface to user(128), provides audio prompts and responses (314), and accepts inputspeech for recognition (315). The example of FIG. 5 includes aspeech-enabled call preparation engine (800) that includes a callpreparation application (802) and a call preparation cockpit (804).Speech-enabled call preparation engine (800) provides a speech interfacethrough the call preparation cockpit (804) through which a user mayprovide oral speech for recognition through microphone (176) and havethe speech digitized through an audio amplifier (185) and acoder/decoder (‘codec’) (183) of a sound card (174) and provide thedigitized speech for recognition to a voice server (151). Speech-enabledcall preparation engine (800) packages digitized speech in recognitionrequest messages according to a VOIP protocol and transmits the speechto voice server (151) through the VOIP connection (216) on the network(100).

Voice server (151) provides voice recognition services forspeech-enabled devices by accepting dialog instructions, VoiceXMLsegments, or the like, and returning speech recognition results,including text representing recognized speech, text for use as variablevalues in dialogs, and output from execution of semantic interpretationscripts as well as voice prompts. Voice server (151) includes computerprograms that provide text-to-speech (‘TTS’) conversion for voiceprompts and voice responses to user input in speech-enabled applicationssuch as, for example, X+V applications, SALT applications, or JavaSpeech applications.

The speech-enabled device (152) in the example of FIG. 5 includes asemantic query engine (298), a module of automated computing machinerythat accepts from the speech-enabled application (195) and executesagainst triple stores (323, 325) semantic queries (340). Thespeech-enabled application (195) formulates semantic queries with userinput from speech (315), GUI, keyboard, mouse (180, 181), or the like. Asemantic query is a query designed and implemented against structureddata. Semantic queries utilize logical operators, namespaces, patternmatching, subclassing, transitive relations, semantic rules, andcontextual full text search. Semantic queries work on named graphs,linked data, or triples. In embodiments of the present invention,triples typically are linked so as to form graphs. This enables asemantic query to process actual relationships between items ofinformation and infer answers from the network of data.

Example formulations of semantic queries are in C, C++, Java, Prolog,Lisp, and so on. The semantic web technology stack of the W3C, forexample, offers SPARQL to formulate semantic queries in a syntax similarto SQL. Semantic queries are used against data structured in triplestores, graph databases, semantic wikis, natural language, andartificial intelligence systems. As mentioned, semantic queries work onstructured data, and in the particular examples of the present case, thestructured data is words described and defined in semantic triplesconnected in ways that conform to a description logic. In manyembodiments of the present invention, semantic queries are assertedagainst data structured according to a description logic that implementsdecidability.

In the example apparatus of FIG. 5, the speech-enabled device is coupledfor data communication through a communications adapter (167), wirelessconnection (118), data communications network (100), and wirelineconnection (121) to a triple server (157). The triple server (157)provides large volume backup for triple stores (323, 325). The tripleserver is a configuration of automated computing machinery thatserializes triples and stores serialized triples in relationaldatabases, tables, files, or the like. The triple server retrieves uponrequest from non-volatile storage such serialized triples, parses theserialized triples into triple stores, and provides such triple storesupon request to speech-enabled devices for use in systems that utilizethe triples in configuring computer memory according to embodiments ofthe present invention.

Call preparation according to embodiments of the present invention,particularly in a thin-client architecture, may be implemented with oneor more voice servers. A voice server is a computer, that is, automatedcomputing machinery, that provides speech recognition and speechsynthesis. For further explanation, therefore, FIG. 6 sets forth a blockdiagram of automated computing machinery comprising an example of acomputer useful as a voice server (151) for a speech-enabled deviceuseful in call preparation according to embodiments of the presentinvention. The voice server (151) of FIG. 6 includes at least onecomputer processor (156) or ‘CPU’ as well as random access memory (168)(‘RAM’) which is connected through a high-speed memory bus (166) and busadapter (158) to processor (156) and to other components of the voiceserver.

Stored in RAM (168) is a voice server application (188), a module ofcomputer program instructions capable of operating a voice server in asystem that is configured for use in configuring memory according tosome embodiments of the present invention. Voice server application(188) provides voice recognition services for multimodal devices byaccepting requests for speech recognition and returning speechrecognition results, including text representing recognized speech, textfor use as variable values in dialogs, and text as stringrepresentations of scripts for semantic interpretation. Voice serverapplication (188) also includes computer program instructions thatprovide text-to-speech (‘TTS’) conversion for voice prompts and voiceresponses to user input in speech-enabled applications such as, forexample, speech-enabled browsers, X+V applications, SALT applications,or Java Speech applications, and so on.

Voice server application (188) may be implemented as a web server,implemented in Java, C++, Python, Perl, or any language that supportsX+V, SALT, VoiceXML, or other speech-enabled languages, by providingresponses to HTTP requests from X+V clients, SALT clients, Java Speechclients, or other speech-enabled client devices. Voice serverapplication (188) may, for a further example, be implemented as a Javaserver that runs on a Java Virtual Machine (102) and supports a Javavoice framework by providing responses to HTTP requests from Java clientapplications running on speech-enabled devices. And voice serverapplications that support embodiments of the present invention may beimplemented in other ways as may occur to those of skill in the art, andall such ways are well within the scope of the present invention.

The voice server (151) in this example includes a natural languageprocessing speech recognition (“NLP-SR”) engine (153). An NLP-SR engineis sometimes referred to in this paper simply as a ‘speech engine.’ Aspeech engine is a functional module, typically a software module,although it may include specialized hardware also, that does the work ofrecognizing and generating human speech. In this example, the speechengine (153) is a natural language processing speech engine thatincludes a natural language processing (“NLP”) engine (155). The NLPengine accepts recognized speech from an automated speech recognition(‘ASR’) engine, processes the recognized speech into parts of speech,subject, predicates, object, and so on, and then converts therecognized, processed parts of speech into semantic triples forinclusion in triple stores.

The speech engine (153) includes an automated speech recognition (‘ASR’)engine for speech recognition and a text-to-speech (‘TTS’) engine forgenerating speech. The speech engine also includes a grammar (104), alexicon (106), and a language-specific acoustic model (108). Thelanguage-specific acoustic model (108) is a data structure, a table ordatabase, for example, that associates speech feature vectors (‘SFVs’)with phonemes representing pronunciations of words in a human languageoften stored in a vocabulary file. The lexicon (106) is an associationof words in text form with phonemes representing pronunciations of eachword; the lexicon effectively identifies words that are capable ofrecognition by an ASR engine. Also stored in RAM (168) is aText-To-Speech (‘TTS’) Engine (194), a module of computer programinstructions that accepts text as input and returns the same text in theform of digitally encoded speech, for use in providing speech as promptsfor and responses to users of speech-enabled systems.

The grammar (104) communicates to the ASR engine (150) the words andsequences of words that currently may be recognized. For furtherexplanation, distinguish the purpose of the grammar and the purpose ofthe lexicon. The lexicon associates with phonemes all the words that theASR engine can recognize. The grammar communicates the words currentlyeligible for recognition. The two sets at any particular time may not bethe same.

Grammars may be expressed in a number of formats supported by ASRengines, including, for example, the Java Speech Grammar Format(‘JSGF’), the format of the W3C Speech Recognition Grammar Specification(‘SRGS’), the Augmented Backus-Naur Format (‘ABNF’) from the IETF'sRFC2234, in the form of a stochastic grammar as described in the W3C'sStochastic Language Models (N-Gram) Specification, and in other grammarformats as may occur to those of skill in the art. Grammars typicallyoperate as elements of dialogs, such as, for example, a VoiceXML <menu>or an X+V <form>. A grammar's definition may be expressed in-line in adialog. Or the grammar may be implemented externally in a separategrammar document and referenced from with a dialog with a URI. Here isan example of a grammar expressed in JSFG:

<grammar scope=“dialog” ><![CDATA[  #JSGF V1.0;  grammar command; <command> = [remind me to] call | phone | telephone <name>  <when>; <name> = bob | martha | joe | pete | chris | john | harold;  <when> =today | this afternoon | tomorrow | next week;  ]]> </grammar>

In this example, the elements named <command>, <name>, and <when> arerules of the grammar. Rules are a combination of a rulename and anexpansion of a rule that advises an ASR engine or a voice interpreterwhich words presently can be recognized. In this example, expansionincludes conjunction and disjunction, and the vertical bars ‘|’ mean‘or.’ An ASR engine or a voice interpreter processes the rules insequence, first <command>, then <name>, then <when>. The <command> ruleaccepts for recognition ‘call’ or ‘phone’ or ‘telephone’ plus, that is,in conjunction with, whatever is returned from the <name> rule and the<when> rule. The <name> rule accepts ‘bob’ or ‘martha’ or ‘joe’ or‘pete’ or ‘chris’ or ‘john’ or ‘harold’, and the <when> rule accepts‘today’ or ‘this afternoon’ or ‘tomorrow’ or ‘next week.’ The commandgrammar as a whole matches utterances like these, for example:

-   -   “phone bob next week,”    -   “telephone martha this afternoon,”    -   “remind me to call chris tomorrow,” and    -   “remind me to phone pete today.”

The voice server application (188) in this example is configured toreceive, from a speech-enabled client device located remotely across anetwork from the voice server, digitized speech for recognition from auser and pass the speech along to the ASR engine (150) for recognition.ASR engine (150) is a module of computer program instructions, alsostored in RAM in this example. In carrying out automated speechrecognition, the ASR engine receives speech for recognition in the formof at least one digitized word and uses frequency components of thedigitized word to derive a speech feature vector or SFV. An SFV may bedefined, for example, by the first twelve or thirteen Fourier orfrequency domain components of a sample of digitized speech. The ASRengine can use the SFV to infer phonemes for the word from thelanguage-specific acoustic model (108). The ASR engine then uses thephonemes to find the word in the lexicon (106).

Also stored in RAM is a VoiceXML interpreter (192), a module of computerprogram instructions that processes VoiceXML grammars. VoiceXML input toVoiceXML interpreter (192) may originate, for example, from VoiceXMLclients running remotely on speech-enabled devices, from X+V clientsrunning remotely on speech-enabled devices, from SALT clients running onspeech-enabled devices, from Java client applications running remotelyon multimedia devices, and so on. In this example, VoiceXML interpreter(192) interprets and executes VoiceXML segments representing voicedialog instructions received from remote speech-enabled devices andprovided to VoiceXML interpreter (192) through voice server application(188).

A speech-enabled application (195 on FIG. 4) in a thin-clientarchitecture may provide voice dialog instructions, VoiceXML segments,VoiceXML <form> elements, and the like, to VoiceXML interpreter (149)through data communications across a network with such a speech-enabledapplication. The voice dialog instructions include one or more grammars,data input elements, event handlers, and so on, that advise the VoiceXMLinterpreter how to administer voice input from a user and voice promptsand responses to be presented to a user. The VoiceXML interpreteradministers such dialogs by processing the dialog instructionssequentially in accordance with a VoiceXML Form Interpretation Algorithm(FIX) (193). The VoiceXML interpreter interprets VoiceXML dialogsprovided to the VoiceXML interpreter by a speech-enabled application.

As mentioned above, a Form Interpretation Algorithm (‘FIA’) drives theinteraction between the user and a speech-enabled application. The FIAis generally responsible for selecting and playing one or more speechprompts, collecting a user input, either a response that fills in one ormore input items, or a throwing of some event, and interpreting actionsthat pertained to the newly filled-in input items. The FIA also handlesspeech-enabled application initialization, grammar activation anddeactivation, entering and leaving forms with matching utterances andmany other tasks. The FIA also maintains an internal prompt counter thatis increased with each attempt to provoke a response from a user. Thatis, with each failed attempt to prompt a matching speech response from auser an internal prompt counter is incremented.

Also stored in RAM (168) is an operating system (154). Operating systemsuseful in voice servers according to embodiments of the presentinvention include UNIX™, Linux™, Microsoft NT™, AIX™, IBM's i5/OS™, andothers as will occur to those of skill in the art. Operating system(154), voice server application (188), VoiceXML interpreter (192), ASRengine (150), JVM (102), and TTS Engine (194) in the example of FIG. 5are shown in RAM (168), but many components of such software typicallyare stored in non-volatile memory also, for example, on a disk drive(170).

Voice server (151) of FIG. 6 includes bus adapter (158), a computerhardware component that contains drive electronics for high-speed buses,the front side bus (162), the video bus (164), and the memory bus (166),as well as drive electronics for the slower expansion bus (160).Examples of bus adapters useful in voice servers according toembodiments of the present invention include the Intel Northbridge, theIntel Memory Controller Hub, the Intel Southbridge, and the Intel I/OController Hub. Examples of expansion buses useful in voice serversaccording to embodiments of the present invention include IndustryStandard Architecture (‘ISA’) buses and Peripheral ComponentInterconnect (‘PCI’) buses.

Voice server (151) of FIG. 6 includes disk drive adapter (172) coupledthrough expansion bus (160) and bus adapter (158) to processor (156) andother components of the voice server (151). Disk drive adapter (172)connects non-volatile data storage to the voice server (151) in the formof disk drive (170). Disk drive adapters useful in voice servers includeIntegrated Drive Electronics (‘IDE’) adapters, Small Computer SystemInterface (‘SCSI’) adapters, and others as will occur to those of skillin the art. In addition, non-volatile computer memory may be implementedfor a voice server as an optical disk drive, electrically erasableprogrammable read-only memory (so-called ‘EEPROM’ or ‘Flash’ memory),RAM drives, and so on, as will occur to those of skill in the art.

The example voice server of FIG. 6 includes one or more input/output(′I/O′) adapters (178). I/O adapters in voice servers implementuser-oriented input/output through, for example, software drivers andcomputer hardware for controlling output to display devices such ascomputer display screens, as well as user input from user input devices(181) such as keyboards and mice. The example voice server of FIG. 5includes a video adapter (209), which is an example of an I/O adapterspecially designed for graphic output to a display device (180) such asa display screen or computer monitor. Video adapter (209) is connectedto processor (156) through a high-speed video bus (164), bus adapter(158), and the front side bus (162), which is also a high-speed bus.

The example voice server (151) of FIG. 6 includes a communicationsadapter (167) for data communications with other computers (182) and fordata communications with a data communications network (100). Such datacommunications may be carried out serially through RS-232 connections,through external buses such as a Universal Serial Bus (‘USB’), throughdata communications data communications networks such as IP datacommunications networks, and in other ways as will occur to those ofskill in the art. Communications adapters implement the hardware levelof data communications through which one computer sends datacommunications to another computer, directly or through a datacommunications network. Examples of communications adapters useful forembodiments of the present invention include modems for wired dial-upcommunications, Ethernet (IEEE 802.3) adapters for wired datacommunications network communications, and 802.11 adapters for wirelessdata communications network communications.

For further explanation, FIG. 7 sets forth a block diagram of automatedcomputing machinery comprising an example of a computer useful as atriple server (157) for call preparation according to embodiments of thepresent invention. The triple server (157) of FIG. 7 includes at leastone computer processor (156) or ‘CPU’ as well as random access memory(168) (‘RAM’) which is connected through a high-speed memory bus (166)and bus adapter (158) to processor (156) and to other components of thetriple server. The processor is connected through a video bus (164) to avideo adapter (209) and a computer display (180). The processor isconnected through an expansion bus (160) to a communications adapter(167), an I/O adapter (178), and a disk drive adapter (172). Theprocessor is connected to a speech-enabled laptop (126) through datacommunications network (100) and wireless connection (118). Disposed inRAM is an operating system (154).

Also disposed in RAM are a triple server application program (297), asemantic query engine (298), a semantic triple store (814), a tripleparser/serializer (294), a triple converter (292), and one or moretriple files (290). The triple server application program (297) accepts,through network (100) from speech-enabled devices such as laptop (126),semantic queries that it passes to the semantic query engine (298) forexecution against the triple stores (323, 325).

The triple parser/serializer (294) administers the transfer of triplesbetween triple stores and various forms of disk storage. The tripleparser/serializer (294) accepts as inputs the contents of triple storesand serializes them for output as triple files (290), tables, relationaldatabase records, spreadsheets, or the like, for long-term storage innon-volatile memory, such as, for example, a hard disk (170). The tripleparser/serializer (294) accepts triple files (290) as inputs and outputsparsed triples into triple stores. In many embodiments, when the tripleparser/serializer (294) accepts triple files (290) as inputs and outputsparsed triples into triple stores, the triple parser/serializer storesthe output triple stores into contiguous segments of memory. Contiguousstorage can be effected in the C programming language by a call to themalloc( ) function. Contiguous storage can be effected by the Pythonbuffer protocol. Contiguous storage can be effected in other ways aswill occur to those of skill in the art, and all such ways are withinthe scope of the present invention. In many embodiments, both triplestores (323, 325) would be stored in one or two segments of contiguousmemory.

For further explanation, FIG. 8 sets forth a functional block diagram ofexample apparatus for call preparation for CRM in computer memory in athick-client architecture according to embodiments of the presentinvention. A thick-client architecture is a client-server architecturein which all or most of the functionality required to administerconfiguring computer memory with triples of a description logic issupported directly on client-side speech-enabled devices rather than onservers. Servers are used for backup and synchronization rather thanspeech recognition or semantic queries. A thick client requiresresources, processor power and storage, possibly not always available ona small device such as a smartwatch. In a thick client with sufficientdata processing resources, however, all pertinent functionality,queries, triples, speech, are typically immediately and fully useful,regardless of network availability.

The thick-client speech-enabled device in the example of FIG. 8 isautomated computer machinery that includes a processor (156), RAM (168),data buses (162, 164, 166, 160), video (180, 209), data communications(167), I/O (178), and disk storage (170). Disposed in RAM are aspeech-enabled call preparation engine (800), a semantic query engine(298), a semantic triple store (814), a triple parser/serializer (294),a triple converter (292), and one or more triple files (290). Thespeech-enabled application (195) accepts from user input semanticqueries that it passes to the semantic query engine (298) for executionagainst the triple store (814). All pertinent triples are available inlocal RAM. All queries succeed or fail based on local storage alone. Noqueries are ever forwarded to the triple server (157). The triple server(157) provides long-term backup and synchronization functions whenmultiple client-side devices share the contents of triple stores, but,for any particular query, all responsive triples are available directlyon the client side.

The triple parser/serializer (294) administers the transfer of triplesbetween triple stores and disk storage (170), and disk storage isavailable directly on the thick client (152) rather than across anetwork on a server. The triple parser/serializer (294) accepts asinputs the contents of triple stores and serializes them for output astriple files (290), tables, relational database records, spreadsheets,or the like, for long-term storage in non-volatile memory, such as, forexample, a hard disk (170). The triple parser/serializer (294) acceptstriple files (290) as inputs and outputs parsed triples into triplestores. In many embodiments, when the triple parser/serializer (294)accepts triple files (290) as inputs and outputs parsed triples intotriple stores, the triple parser/serializer stores the output triplestores into contiguous segments of memory.

The speech-engine engine (153) is a full-service NLP-SR engine thatincludes natural language processing (155), speech recognition (150), agrammar (104), a lexicon (106), a model (108), and text-to-speechprocessing (194), all as described in more detail above. Thethick-client speech-enabled device (152) has no need to reach across anetwork for speech-related processing. Full speech enablement isavailable directly on the speech-enabled device itself.

For further explanation, FIG. 9 sets forth a flow chart illustrating anexample method for call preparation for customer relationship management(‘CRM’) according to embodiments of the present invention. The method ofFIG. 9 includes invoking (302), by a call preparation application (802),an intelligence assistant to retrieve lead details, customerinformation, and insights for use during a call between a tele-agent anda customer. Invoking (302) an intelligence assistant may be carried outby establishing communications with the intelligence assistant andproviding to the intelligence assistant information necessary toidentify and receive lead details, customer information, and insights.Such information may include an identification of the tele-agent,campaign the tele-agent is supporting, the customer, the client, andmany others as will occur to those of skill in the art.

Once invoked, the intelligence assistant may provide additionalinformation upon its own motion, on request, periodically, or in otherways as will occur to those of skill in the art. For example, theintelligence assistant may parse text converted from speech of atele-agent into semantic triples in dependence upon a call preparationtaxonomy and a call preparation ontology and store the parsed triples inan enterprise knowledge graph of a semantic graph database. Theintelligence assistant my then develop real-time queries and query thesemantic database with the real-time queries and identify insights andother useful information in dependence upon the results of the real-timequeries. Furthermore, the intelligence assistant may identify one ormore third-party resources such as industry resources, social mediaresources, and the like and query the identified resources forinformation relevant to the call between the tele-agent and thecustomer.

The method of FIG. 9 includes administering (304), by the callpreparation application (802), tele-agent call preparation notes for useduring the call between the tele-agent and the customer. Administering(304) tele-agent call preparation notes may be carried out by retrievingpreviously prepared call notes, receiving call notes from thetele-agent, identifying standard or pre-prepared call note templates, orin other ways as will occur to those of skill in the art. In someembodiments, the call preparation application is configured to receive,through the call preparation cockpit application, speech for recognitionfrom the tele-agent and invoke an automated speech recognition engine toconvert the speech for recognition into text in dependence upon one ormore call preparation grammars and store text as call preparation notesfor use by the tele-agent.

The method of FIG. 9 includes receiving (906), by a call preparationcockpit application (804) from the call preparation application, leaddetails, customer information, insights, and call notes for use duringthe call between the tele-agent and the customer displaying (908),through a call preparation cockpit, the lead details, customerinformation, insights and tele-agent call preparation notes. Displaying(908), through a call preparation cockpit, the lead details, customerinformation, insights and tele-agent call preparation notes may becarried out by rendering an XML or other markup language page containingthe lead details, customer information, insights, and call preparationnotes.

In the example of FIG. 9, the call preparation application (802) mayretrieve customer information from one or more customer relationshipmanagement (‘CRM’) systems (806). Such customer information may includeuse cases, sales histories, known technology used by the customer andother useful information for the tele-agent in the upcoming call;

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

What is claimed is:
 1. A call preparation engine for customerrelationship management (“CRM”), the call preparation engine comprisinga module of automated computing machinery stored on one or morenon-transitory computer-readable mediums, the call preparation engineincluding: a call preparation application comprising a module ofautomated computing machinery stored on a non-transitorycomputer-readable medium configured to invoke an intelligence assistantto retrieve lead details, customer information, and insights for useduring a call between a tele-agent and a customer and administertele-agent call preparation notes for use during the call between thetele-agent and the customer; and a call preparation cockpit applicationcomprising a module of automated computing machinery stored on anon-transitory computer-readable medium configured to receive, from thecall preparation application, lead details, customer information,insights, and call notes for use during the call between the tele-agentand the customer and display, through a call preparation cockpit, thelead details, customer information, insights and tele-agent callpreparation notes.
 2. The call preparation engine of claim 1 wherein thecall preparation application is configured to received, through the callpreparation cockpit application, speech for recognition from thetele-agent and invoke an automated speech recognition engine to convertthe speech for recognition into text in dependence upon one or more callpreparation grammars.
 3. The call preparation engine of claim 2 whereinthe intelligence assistant further comprises a triple parser and aninsight generator, and wherein: the triple parser is configured to parsetext converted from speech of a tele-agent into semantic triples independence upon a call preparation taxonomy and a call preparationontology and store the parsed triples in an enterprise knowledge graphof a semantic graph database; and the insight generator is configured todevelop real-time queries and query the semantic database with thereal-time queries and identify insights in dependence upon the resultsof the real-time queries.
 4. The call preparation engine of claim 3wherein the insight generator is configured to develop real-time queriesin dependence upon text converted from speech between the tele-agent andthe customer.
 5. The call preparation engine of claim 3 wherein theinsight generator is configured to develop real-time queries independence upon customer information updated during the tele-agent andthe customer.
 6. The call preparation engine of claim 1 wherein theintelligence assistant further comprises a third-party data retrievalmodule, a module of automated computing machinery stored on one or morenon-transitory computer-readable mediums configured to identify one ormore third-party resources and query the identified resources forinformation relevant to the call between the tele-agent and thecustomer.
 7. The call preparation engine of claim 4 wherein thethird-party resources includes one or more social media platforms. 8.The call preparation engine of claim 4 wherein information relevant tothe call between the tele-agent and the customer includes industryinsights.
 9. The call preparation engine of claim 1 wherein the callpreparation application is configured to retrieve customer informationfrom one or more customer relationship management (‘CRM’) systems. 10.The call preparation engine of claim 7 wherein the call preparationapplication is configured to receive, from the intelligence assistant ora CRM, one or more for use cases for the tele-agent in the upcomingcall; and wherein the call preparation cockpit application is furtherconfigured to receive, from the call preparation application, the usescases and display, through a call preparation cockpit, the use cases.11. A method for customer relationship management (“CRM”) comprising:invoking an intelligence assistant to retrieve lead details, customerinformation, and insights for use during a call between a tele-agent anda customer; administering tele-agent call preparation notes for useduring the call between the tele-agent and the customer; and displaying,through a call preparation cockpit, the lead details, customerinformation, insights and tele-agent call preparation notes.
 12. Themethod of claim 11 further comprising receiving speech for recognitionfrom the tele-agent and invoking an automated speech recognition engineto convert the speech for recognition into text in dependence upon oneor more call preparation grammars.
 13. The method of claim 11 furthercomprising parsing text converted from speech of a tele-agent intosemantic triples in dependence upon a call preparation taxonomy and acall preparation ontology and storing the parsed triples in anenterprise knowledge graph of a semantic graph database.
 14. The methodof claim 11 further comprising developing real-time queries, queryingthe semantic database with the real-time queries and identifyinginsights in dependence upon the results of the real-time queries. 15.The method of claim 14 further comprising developing real-time queriesin dependence upon text converted from speech between the tele-agent andthe customer.
 16. The method of claim 14 further comprising developingreal-time queries in dependence upon customer information updated duringthe tele-agent and the customer.
 17. The method of claim 11 furthercomprising identifying one or more third-party resources and queryingthe identified resources for information relevant to the call betweenthe tele-agent and the customer.
 18. A system for call preparationcomprising: a call preparation engine configured to receive and displayreal-time customer information to a tele-agent and real-time insights;an intelligence assistant configured to administer real-time queries andgenerate real-time insights; and a semantic graph database configured toreceive from the intelligence assistant real-time queries and returnresults to the intelligence assistant information to generate insights.19. The system of claim 18 further comprising a speech engine configuredto receive speech for recognition from the tele-agent and invoking anautomated speech recognition engine to convert the speech forrecognition into text in dependence upon one or more call preparationgrammars.
 20. The system of claim 19 wherein the intelligence assistantis further configured to parse text converted from speech of atele-agent into semantic triples in dependence upon a call preparationtaxonomy and a call preparation ontology and storing the parsed triplesin an enterprise knowledge graph of a semantic graph database.